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HSMAI Section: Overdue Or Overwrought? Toward A Marketing And Analytics Capability Maturity Model

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October 25, 2016
Max Rayner - max.rayner@hudsoncrossing.com

Marketing is undergoing a transition that bears some similarity to what happened to IT as software became more central to enterprise success


Marketing has been the purview of “creatives” but when it’s done at internet scale, creativity without discipline can be disastrous.

There are good frameworks for both creation of new processes and for optimization of existing ones that have long been applied to largescale IT and industrial production.

Hudson Crossing has developed a Capability Maturity Model aligned to Six Sigma which allows clients to diagnose marketing and analytics maturity and create a time-ordered roadmap toward excellence.

It’s probably high time to require that marketing meets quality, reliability and predictability industrial strength standards. Manufacturing and IT went through this transition first, but it’s marketing’s turn: When you’re spending rivers of money at internet scale, creativity alone is not just insufficient, it’s dangerous.  Just as IT’s early days were full of promise and led to sudden budget increases and ambitious transformational projects, the advent of digital marketing has led to vast increases in marketing and distribution budgets and matching ambitious projects.

In the 1980s it became clear that while software was becoming central to enterprises of all types, it was quite often less than well managed.  More and more complex projects either failed or were delivered late, over budget and under spec. Worse, the ability to evolve was often missing, even if the initial delivery was successful.

In an effort to raise the level of IT management from an ad hoc art to a repeatable high-quality industrial process, the U. S. Department of Defense sponsored the development of a Capability Maturity Model (CMM) at Carnegie Mellon University. Meanwhile, Motorola saw the introduction of Six Sigma (6s), to be followed by GE and many other companies.

The CMM (originally focused on software engineering) has since been expanded to apply generally to various processes under the initials CMMI, Capability Maturity Model Integration, while 6s’s DMAIC process improvement (Define –Measure – Analyze – Improve – Control) and DMADV process design (Define – Measure – Analyze – Design – Verify) also got generalized to products, services and processes.

At Hudson Crossing, we observe a significant increase in travel, hospitality and e-commerce clients that are spending so much on marketing and distribution that, we can paraphrase the saying, “a million here, a million there, and pretty soon you’re talking real money.”

In order to simplify diagnosing our clients’ situations, we have adapted CMMI and 6s concepts to today’s digital marketing and big data analytics world.  This article provides a summary.

For the purists, the logical relationship
between CMMI and 6s may need
some elaboration, so let’s look at
some foundational background first.

At the highest conceptual level
these can be elaborated as follows:


Non-performing, or completely ad hoc marketing and analytics practices.
Performing Work
Performance may not be stable and may not meet specific objectives such as ROI, total cost effectiveness and schedule, but useful work is occurring.
Managed Work
Marketing and analytics tasks are planned, performed, monitored and controlled for individual projects, groups, or stand-alone processes to achieve loyalty, distribution, revenue management and branding purposes.  Both overall objectives and other KPIs, such as revenue after marketing spend, cost, schedule and quality are managed .
Managed with Defined Standards
Marketing and analytics are not only managed but their goals are driven by fully documented standard processes.  Goals, activities, campaigns and use of third parties are tailored to the enterprise’s priorities and outcomes are measured so that deviations in overall spending and revenue after marketing beyond those allowed by formal guidelines are documented, justified, reviewed and approved.
Quantitatively Managed:
KPI-driven from High Integrity Data
Marketing and analytics are quantitatively managed and driven by defined processes analyzed with statistical and other quantitative techniques.  Data engineering produces reliable analytics to evaluate overall and individual outcomes of marketing activities across loyalty, distribution, revenue management and branding.  Activities are understood in statistical terms, and are controlled throughout their entire life cycle.
Enhancing Enterprise Value
Marketing and analytics processes are optimized on an ongoing basis to maximize enterprise value.  Marketing and analytics activities and initiatives are quantitatively managed processes that are improved based on root cause analysis and corrective action for any unexpected process variation.  Marketing executives focus on continually improving enterprise outcomes through both incremental and innovative improvements. New creative efforts are subject to clear Define - Measure - Analyze - Design - Verify criteria and standard execution processes are ongoing targets of Define - Measure - Analyze - Improve - Control improvement activities.

Marketing and Analytics Sample Litmus Tests

When there's a business performance issue, do you call the CMO first or do you call the CFO and start looking for cuts instead of more profitable growth?

When you're making judgements across domains (such as looking at how satisfaction/ NPS might impact the top line) is data easily available?

Are you afraid of big data or already leveraging (directly or indirectly via an agency) DMPs/ DSPs and the whole alphabet soup of internetscale marketing platforms?

Is PPC a black-box that you both resent and are afraid of cutting off, or is the data available to clearly understand revenue after marketing (i. e., variable profit after marketing but before G&A expense)?

Do you have enough data to make reasonable attributions and compare the costs and benefits of filling a bed via marketing, B2C channels, B2C channels, groups, etc?

Each case leads to different mapping between the answer to the questions below and a diagnoses of capability/maturity in marketing and analytics. These are some of the key questions we often use to get discussions going:

Starting with “tone at the top,” how much do marketing and analytics contribute to key management decisions?
Do inputs from the CMO’s organization play a leading role in corporate planning and initiatives to enhance enterprise value?

Is marketing and user experience data broadly accessible without IT involvement being required to answer most business questions? Do the CMO and CFO live in the same reality and enjoy broad agreement of transactional and marketing analytics data?

Do you already enjoy enterprise integration of marketing insights? Is there clear data visibility and dimensional alignment across topics?
How easy is it for user experience detail such as real user measurement data to be joined to overall web analytics, PPC and social engagement data? How well can these be broadly aligned with time-series of transactional and financial data?

Do you have near real-time autonomic data engineering processes that bring together creative initiatives, marketing campaigns, financial outcomes and channel transactional and CRM details?

Are the connections between guest satisfaction/NPS surveys, return rates and ADRs and occupancies versus your competitive sets easy to analyze and leverage for improved results?

Are digital marketing platforms, internet-scale signals and big data leveraged to plan, measure and optimize performance?
Beyond financial transactions, CRM and loyalty data, do you leverage both anonymous and identified data, micro-segments and real-time prospect/guest activity on social media?

Do you (or your external agencies) use data management platforms (DMPs), demand side platforms (DSPs), multivariate ad generation and testing and programmatic creative platforms? Are you able to engage in mass customizations within micro-segments?

Does marketing lead the effort to managed data, apply consistent definitions and minimize disconnects?
Is there evidence of proactive management of an enterprisewide data model with repeatable, autonomic processes that expose issues and drive RCCA for any data disconnects between marketing and other enterprise functions?

Is the company collecting and organizing a permanent record of its search engine result page entries so it can relate content and other moves to ongoing improvements in desired primary search and long tail terms?

How well do marketing and analytics leverage operational details such as folio data to analyze and proactively enhance guest lifetime value?
Small changes in median guest lifetime value can lead to huge changes in enterprise value.

This is true of both prospective value (guests who may be starting in life now but are likely to increase their spending over time) and current value (prearrival, current stay).

Are you able to personalize guest engagement and experiences beyond a single booking cycle by leveraging all available data to personalize the lifetime engagement of each prospect/guest?

Are you optimizing marketing outreach, distribution options and channel management?
Do marketing and analytics work hand in hand with revenue management and sales to leverage various options across seasons, channels and outbound campaigns?

Do you have an overall view of what demographics and which individuals are being targeted and specifically engaged? Are you able to leverage actual data to optimize engagement for lifetime returns rather than disconnected individual campaigns?

Are you controlling and optimizing revenue after marketing expense and setting goals for marketing executives with measured, specific improvements in outcomes that include internal and external benchmarks?

Finally, how ad hoc or data-driven is content generation?
Everybody knows that content needs to be optimized depending on the form factor that it’s going to be displayed on.

Less obvious is the need to let data drive content. Persuasive marketing is not a dark art. Are you leveraging autonomic platforms that test and target content?


Marketing creativity, which we wholeheartedly endorse, should not become an excuse for lack of accountability.

Just as IT had to grow up when it started to really matter (and cost accordingly), it’s time to insist on the maximum possible level of capability/maturity in marketing.

Contrary to fears we occasionally hear expressed by marketing execs and creatives, this does not take the fun out of marketing. To the contrary, the more convincingly the benefits of a creative idea can be measured, the easier it is to get enterprisewide support for it – and all the matching resources that come with that.

Wherever we’ve seen marketing executives avoid the discipline of something akin to a capability/maturity model, we often see erratic and unpleasant outcomes. Wherever we’ve seen CMOs embrace the path to full capability/maturity optimization, positive results have followed not just within marketing as a function, but with respect to improvements in overall enterprise value.

MAX RAYNER is a partner at Hudson Crossing LLC, a leading consultancy in travel and hospitality. He can be reached at max.rayner@hudsoncrossing.com

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Benefits of Moving Up the Capability/ Maturity Ladder

The business benefits of driving toward higher levels of capability/maturity have been observed.


In one case, a data audit exposed that PPC vendors were claiming 500 percent credit for the company’s revenue. The attribution model used would only make sense if the company was grossing 5 times higher. Exposing the fallacy allowed the company to repurpose $1.5M of useless marketing spend to campaigns that actually contributed bookings.

In another case Hudson Crossing observed, marketing executives were madly in love with a content push, but their customers were not. Auditing campaign results matched to content showed that an opportunity does far better with what everybody thought were “boring” visuals. Tested, demonstrably successful approaches work best.


One company was being told that every single campaign was “above average.” In other words, although finance numbers showed overall conversion rates were a shade below 3 percent, marketing and distribution analytics showed all channels to be well above industry norms. An audit turned out that averages were not weighted, and small but successful channels and campaigns were being given as much weight as massive but lousy ones.

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